Many current efforts ongoing within the information technology community include considerable interest in the concept of “intelligent workload management.” In particular, much of the recent development in the information technology community has focused on providing better techniques to intelligently mange “cloud” computing environments, which generally include dynamically scalable virtualized resources that typically provide network services. For example, cloud computing environments often use virtualization as the preferred paradigm to host workloads on underlying physical hardware resources. For various reasons, computing models built around cloud or virtualized data centers have become increasingly viable, including cloud infrastructures can permit information technology resources to be treated as utilities that can be automatically provisioned on demand. Moreover, cloud infrastructures can limit the computational and financial cost that any particular service has to the actual resources that the service consumes, while further providing users or other resource consumers with the ability to leverage technologies that could otherwise be unavailable. Thus, as cloud computing and storage environments become more pervasive, many information technology organizations will likely find that moving resources currently hosted in physical data centers to cloud and virtualized data centers can yield economies of scale, among other advantages.
Nonetheless, although many efforts in the information technology community relates to moving towards cloud and virtualized computing environments, existing systems tend to fall short in adequately addressing concerns relating to managing or controlling workloads and storage in such environments. For example, cloud infrastructures are typically designed to support generic business practices, which can prevent individuals and organizations from having the ability to suitably change or otherwise manage important aspects associated the cloud computing platforms. Moreover, concerns regarding performance, latency, reliability, and security tend to present significant challenges because outages and downtime can lead to lost business opportunities and decreased productivity, while generic cloud computing platforms may present concerns relating to governance, risk, and compliance. In other words, although the “state of the art” in instrumentation and management may be appropriate for workloads deployed in corporate data centers, the lack of visibility into cloud and virtualized data centers may result in significant management problems. As such, techniques currently used to instrument and manage workloads deployed in corporate data centers typically do not scale in cloud computing environments.
For example, FIG. 1 illustrates an exemplary existing system 100 typically used to structure workloads that have been deployed in virtualized data centers (e.g., a virtualized corporate computing infrastructure). In particular, the typical existing system 100 generally includes a data center manager (or orchestrator) 110 that has responsibility to manage one or more virtualization hosts (or management domains) 120. For example, the data center manager 110 may generally manage decisions that relate to deploying workloads 140 to the virtualization hosts and managing physical hardware and hypervisor resources 130 that have been assigned or otherwise allocated to the workloads 140. In many cases, the managed workloads 140 will have various attributes, parameters, or other constraints that relate to the physical hardware and hypervisor resources 130 that the workloads 140 require (e.g., a particular managed workload 140 may include a service level agreement that defines minimum storage capacities, memory requirements, network bandwidth, or other resource parameters). As such, the existing system 100 shown in FIG. 1 typically instruments the virtualization host 120 and the managed workload 140 to collect utilization metrics relating to the physical hardware and hypervisor resources 130 allocated to the workloads 140. The utilization metrics that the virtualization host 120 and the managed workload 140 collect would then be provided to the data center manager 110, which applies any specified policies relevant to determining whether to provision or tune the resources allocated to the workload (e.g., increasing storage capacity allocated to the workload 140 in response to the currently allocated storage capacity violating a service level agreement).
To close the resource management loop, the existing system 100 shown in FIG. 1 typically has the data center manager 110 communicate with the virtualization host 120 to effect provisioning or otherwise tuning the physical hardware and hypervisor resources 130 allocated to the managed workload 140. Accordingly, although the existing system 100 shown in FIG. 1 can provision or otherwise tune resources that have been assigned or allocated to managed workloads 140, the management infrastructure used therein suffers from several drawbacks. In particular, the management infrastructure that the existing system 100 uses to provision or otherwise tune resources allocated to managed workloads 140 lacks scalability because the data center manager 110 often becomes a bottleneck, especially in computing environments that have substantial quantities of workloads 140 or virtualization hosts 120 that need management. Moreover, in many scenarios, the management infrastructure shown in FIG. 1 may be impossible to deploy in cloud and virtualized data centers because cloud and virtualized data centers tend to provide little or no visibility into the underlying infrastructure, especially the abstracted physical hardware and hypervisor resources 130 allocated to the workloads 140 that may be deployed therein.